| Records |
| Author |
Carvallo, C.; Jalil-Vega, F.; Moreno, R. |
| Title |
A multi-energy multi-microgrid system planning model for decarbonisation and decontamination of isolated systems |
Type |
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| Year |
2023 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
343 |
Issue |
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Pages |
121143 |
| Keywords |
Multi-energy systems; Multi-microgrid systems; Integrated energy systems model; Decarbonisation; Decontamination; Energy planning |
| Abstract |
Decarbonising and decontaminating remote regions in the world presents several challenges. Many of these regions feature isolation, dispersed demand in large areas, and a lack of economic resources that impede the development of robust and sustainable networks. Furthermore, isolated systems in the developing world are mostly based on diesel generation for electricity, and firewood and liquefied petroleum gas for heating, as these options do not require a significant infrastructure cost. In this context, we present a stochastic multi-energy multi-microgrid system planning model that integrates electricity, heat and hydrogen networks in isolated systems. The model is stochastic to capture uncertainty in renewable generation outputs, particularly hydro and wind, and thus design a multi-energy system proved secured against such uncertainty. The model also features two distinct constraints to limit the emissions of CO2 (for decarbonisation) and particulate matter (for decontamination), and incorporates firewood as a heating source. Moreover, given that the focus is on low-voltage networks, we introduce a fully linear AC power flow equations set, allowing the planning model to remain tractable. The model is applied to a real-world case study to design a multi-energy multi-microgrid system in an isolated region in Chilean Patagonia. In a case with a zero limit over direct CO2 emissions, the total system's cost increases by 34% with respect to an unconstrained case. In a case with a zero limit over particulate matter emissions, the total system's cost increases by 189%. Finally, although an absolute zero limit over both, particulate matter and direct CO2 emissions, leads to a total system's cost increase of 650%, important benefits in terms of decarbonisation and decontamination can be achieved at marginal cost increments. |
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0306-2619 |
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| Notes |
WOS:001001406500001 |
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UAI @ alexi.delcanto @ |
Serial |
1820 |
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| Author |
Go, R.S.; Munoz, F.D.; Watson, J.P. |
| Title |
Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards |
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| Year |
2016 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
183 |
Issue |
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Pages |
902-913 |
| Keywords |
Energy storage; Renewable portfolio standards; Transmission expansion planning |
| Abstract |
Worldwide, environmental regulations such as Renewable Portfolio Standards (RPSs) are being broadly adopted to promote renewable energy investments. With corresponding increases in renewable energy deployments, there is growing interest in grid-scale energy storage systems (ESS) to provide the flexibility needed to efficiently deliver renewable power to consumers. Our contribution in this paper is to introduce a unified generation, transmission, and bulk ESS expansion planning model subject to an RPS constraint, formulated as a two-stage stochastic mixed-integer linear program (MILP) optimization model, which we then use to study the impact of co-optimization and evaluate the economic interaction between investments in these three asset classes in achieving high renewable penetrations. We present numerical case studies using the 24-bus IEEE RTS-96 test system considering wind and solar as available renewable energy resources, and demonstrate that up to $180 million/yr in total cost savings can result from the co-optimization of all three assets, relative to a situation in which no ESS investment options are available. Surprisingly, we find that co-optimized bulk ESS investments provide significant economic value through investment deferrals in transmission and generation capacity, but very little savings in operational cost. Finally, we observe that planning transmission and generation infrastructure first and later optimizing ESS investments as is common in industry captures at most 1.7% ($3 million/yr) of the savings that result from co-optimizing all assets simultaneously. (C) 2016 Elsevier Ltd. All rights reserved. |
| Address |
[Go, Roderick S.] Johns Hopkins Univ, Baltimore, MD 21218 USA, Email: rgo@jhu.edu; |
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Elsevier Sci Ltd |
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English |
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0306-2619 |
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| Notes |
WOS:000395726400003 |
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| Call Number |
UAI @ eduardo.moreno @ |
Serial |
718 |
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| Author |
Grigioni, I.; Polo, A.; Dozzi, M.V.; Stamplecoskie, K.G.; Jara, D.H.; Kamat, P.V.; Selli, E. |
| Title |
Enhanced Charge Carrier Separation in WO3/BiVO4 Photoanodes Achieved via Light Absorption in the BiVO4 Layer |
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| Year |
2022 |
Publication |
ACS Applied Energy Materials |
Abbreviated Journal |
ACS Appl. Energy Mater. |
| Volume |
5 |
Issue |
11 |
Pages |
13142–13148 |
| Keywords |
solar water oxidation; heterojunction; ultrafast transient absorption; photoactive layer thickness; filter effect |
| Abstract |
Photoelectrochemical (PEC) water splitting converts solar light and water into oxygen and energy-rich hydrogen. WO3/BiVO4 heterojunction photoanodes perform much better than the separate oxide components, though internal charge recombination undermines their PEC performance when both oxides absorb light. Here we exploit the BiVO4 layer to sensitize WO3 to visible light and shield it from direct photoexcitation to overcome this efficiency loss. PEC experiments and ultrafast transient absorption spectroscopy performed by frontside (through BiVO4) or backside (through WO3) irradiating photoanodes with different BiVO4 layer thickness demonstrate that irradiation through BiVO4 is beneficial for charge separation. Optimized electrodes irradiated through BiVO4 show 40% higher photocurrent density compared to backside irradiation. |
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2574-0962 |
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UAI @ alexi.delcanto @ |
Serial |
1732 |
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| Author |
Guevara, E.; Babonneau, F.; Homem-de-Mello, T.; Moret, S. |
| Title |
A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty |
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| Year |
2020 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
271 |
Issue |
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Pages |
18 pp |
| Keywords |
Strategic energy planning; Electricity generation; Uncertainty; Distributionally robust optimization; Machine learning |
| Abstract |
This paper investigates how the choice of stochastic approaches and distribution assumptions impacts strategic investment decisions in energy planning problems. We formulate a two-stage stochastic programming model assuming different distributions for the input parameters and show that there is significant discrepancy among the associated stochastic solutions and other robust solutions published in the literature. To remedy this sensitivity issue, we propose a combined machine learning and distributionally robust optimization (DRO) approach which produces more robust and stable strategic investment decisions with respect to uncertainty assumptions. DRO is applied to deal with ambiguous probability distributions and Machine Learning is used to restrict the DRO model to a subset of important uncertain parameters ensuring computational tractability. Finally, we perform an out-of-sample simulation process to evaluate solutions performances. The Swiss energy system is used as a case study all along the paper to validate the approach. |
| Address |
[Guevara, Esnil] Univ Adolfo Ibanez, PhD Program Ind Engn & Operat Res, Santiago, Chile, Email: frederic.babonneau@uai.cl |
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Elsevier Sci Ltd |
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English |
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0306-2619 |
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WOS:000540436500003 |
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UAI @ eduardo.moreno @ |
Serial |
1188 |
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Navarro, A.; Favereau, M.; Lorca, A.; Olivares, D.; Negrete-Pincetic, M. |
| Title |
Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks |
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| Year |
2024 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
358 |
Issue |
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Pages |
122554 |
| Keywords |
OR in energy; Hydrothermal scheduling; Optimization under uncertainty; Renewable energy; Stochastic dual dynamic programming |
| Abstract |
The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium -term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed shortterm operational model to be correctly linked to the proposed medium -term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium -term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations. |
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0306-2619 |
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WOS:001154000300001 |
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UAI @ alexi.delcanto @ |
Serial |
1946 |
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| Author |
Salgado, M.; Negrete-Pincetic, M.; Lorca, A.; Olivares, D. |
| Title |
A Low-complexity Home Energy Management System for Electricity Demand Side Aggregators |
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| Year |
2021 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
2021 |
Issue |
294 |
Pages |
116985 |
| Keywords |
Home energy management system; Demand response; Load following; Demand aggregator |
| Abstract |
A low-complexity decision model for a Home Energy Management System is proposed to follow demand trajectory sets received from a Demand Side Response aggregator. This model is designed to reduce its computational complexity and being solved by low performance processors using available Single-Board Computers as a proof of concept. To decrease the computational complexity is proposed a two-stage model, where the first stage evaluates the hourly appliance scheduling using a relaxed set of restrictions, and the second stage evaluates a reduced set of appliances in a intra-hourly interval with a detailed characterization of the scheduled appliance properties. Simulations results show the effectiveness of the proposed algorithm to follow trajectories for different sets of home appliances and operational conditions. For the studied cases, the model presents deviations in the demand for the 3.2% of the cases in the first-stage and a 12% for the second-stage model. Results show that the proposed model can schedule available appliances according to the demand aggregator requirements in a limited solving time with diverse hardware. |
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0306-2619 |
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Approved |
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UAI @ alexi.delcanto @ |
Serial |
1400 |
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| Author |
Salgado, M.; Negrete-Pincetic, M.; Lorca, A.; Olivares, D. |
| Title |
A low-complexity decision model for home energy management systems |
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| Year |
2021 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
294 |
Issue |
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Pages |
116985 |
| Keywords |
Home energy management system; Demand response; Load following; Demand aggregator |
| Abstract |
A low-complexity decision model for a Home Energy Management System is proposed to follow demand trajectory sets received from a Demand Side Response aggregator. This model is designed to reduce its computational complexity and being solved by low performance processors using available Single-Board Computers as a proof of concept. To decrease the computational complexity is proposed a two-stage model, where the first stage evaluates the hourly appliance scheduling using a relaxed set of restrictions, and the second stage evaluates a reduced set of appliances in a intra-hourly interval with a detailed characterization of the scheduled appliance properties. Simulations results show the effectiveness of the proposed algorithm to follow trajectories for different sets of home appliances and operational conditions. For the studied cases, the model presents deviations in the demand for the 3.2% of the cases in the first-stage and a 12% for the second-stage model. Results show that the proposed model can schedule available appliances according to the demand aggregator requirements in a limited solving time with diverse hardware. |
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0306-2619 |
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WOS:000663303000007 |
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UAI @ alexi.delcanto @ |
Serial |
1427 |
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| Author |
Wickham, D.; Hawkes, A.; Jalil-Vega, F. |
| Title |
Hydrogen supply chain optimisation for the transport sector-Focus on hydrogen purity and purification requirements |
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| Year |
2021 |
Publication |
Applied Energy |
Abbreviated Journal |
Appl. Energy |
| Volume |
305 |
Issue |
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Pages |
117740 |
| Keywords |
Hydrogen; hydrogen-to-vehicle; Gas distribution network; Hydrogen purity; Hydrogen purification; Hydrogen supply chain |
| Abstract |
This study presents a spatially-resolved optimisation model to assess cost optimal configurations of hydrogen supply chains for the transport sector up to 2050. The model includes hydrogen grades and separation/purification technologies, offering the possibility to assess the effects that hydrogen grades play in the development of cost-effective hydrogen supply chains, including the decisions to repurpose gas distribution networks or blending hydrogen into them. The model is implemented in a case study of Great Britain, for a set of decarbonisation and learning rate scenarios. A base case with a medium carbon price scenario shows that the total discounted cost of the hydrogen supply chain is significantly higher than shown in previous studies, largely due to the additional costs from purification/separation needed to meet hydrogen purity standards for transport applications. Furthermore, it was shown that producing hydrogen from steam methane reforming with carbon capture and storage; installing new transmission pipelines; repurposing the gas distribution network to supply 100% hydrogen; and purifying hydrogen with a pressure swing adsorption system locally at the refuelling station; is a cost optimal configuration for the given technoeconomic assumptions, providing hydrogen at 6.18 pound per kg at the pump. Purification technologies were found to contribute to 14% and 30% of total discounted investment and operation costs respectively, highlighting the importance of explicitly including them into hydrogen supply chain models for the transport sector. |
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0306-2619 |
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WOS:000702843800006 |
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UAI @ alexi.delcanto @ |
Serial |
1473 |
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